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#3 of 15 AI agencies in Warsaw

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Sparkbit

Warsaw, Poland

10-49€85-130/hrFounded 2014

About

Warsaw software house delivering machine-learning systems and R&D prototypes, with a research-heavy team drawn from Polish scientific universities. Works computer vision, time-series and retrieval problems alongside backend architecture.

Who They Work With

Early-stage product companies that need the ML core of their product built, tested and handed over with the IP.

Engagements:Project-based

Based on 5 client engagements with a source URL · researched 23 Sept 2026

Our Take

Sparkbit publishes ML case studies with the model, the metric and the handover spelled out: for 5x5 Technologies it retrained a YOLOv8 tower-equipment detector from F1 0.72 to 0.86 over a 12-month engagement on a telecom digital-twin platform, and for the New York health-tech startup phy it built a posture-analysis core covering 20+ conditions whose algorithms went into the client's FDA application.

The studies also show where work stopped: the anti-counterfeit label project ended after discovery when the findings argued against continuing, and the context-aware telematics system was a grant-funded R&D program whose hardware rollout, the study says, was held back by the supply chain.

Most dated AI work falls in 2020–2022; the LLM-era record is an anonymized RAG assistant over 7,000+ PDFs for a Swiss device manufacturer (2025), described as in production, and an Italist data-quality pipeline whose GPT-4 stack sits under a 2020–2022 label.

The weaker fit is buyers who need business outcomes measured in money or hours—results here are model metrics and qualitative gains—or a named reference for a recent LLM deployment.

Key strengths

  • Model metrics are published with the stack: F1 0.72 to 0.86 at 5x5, about 0.85–0.95 F1 on key telematics perception functions, and ~8k concurrent users sustained with under 0.002% timeouts
  • Five client executives are quoted by name and title—including 5x5 Technologies' CEO, Italist's CTO and Spicerr's CEO—four of them on the case study their quote refers to
  • The services page lists what is handed over—repos, infrastructure-as-code, test suite, dashboards, runbooks and IP assignment—and the 5x5 study reports that handover took place

Good to know

  • No founder or engineer is named anywhere on the site—ask who would lead your project and to see that person's prior ML work
  • The Italist study lists GPT-4 while the index dates it 2020–2022 and files it under Health-tech—ask for the dates and scope of the LLM work specifically
  • Outcomes such as "duplicates down" and "operational savings" carry no figures—ask each reference what the system changed in hours, cost or error rate
Gabor Kiss

Reviewed by Gabor Kiss

Founder & Curator, AIAgencies.eu · 23 Sept 2026

Based on the agency's publicly available portfolio. Own this agency? Claim your profile to provide additional context or request a review update.


How we evaluate agencies

Notable Clients

Every engagement documented in a published case study

Documented engagements: 5x5 Technologies, phy, Italist, Spicerr, and Altermobili. All 5 carry a source URL on file.

5x5 Technologies12-month extension of a telecom-tower digital-twin platform: YOLOv8 equipment detection retrained from F1 0.72 to 0.86, RTK alignment, lease-compliance checks, Revit/AutoCAD outputs, staging and CI, documented handover; CEO quotedSource
phyML core for a posture-assessment app: 3D scan to detection of 20+ musculoskeletal conditions and a personalized exercise plan, deep learning plus geometric methods; algorithms included in the client's FDA application; co-founder quotedSource
ItalistIngest-time pipeline de-duplicating SKUs with text and image embeddings and a learned pair classifier, plus guardrailed GPT-4 content enrichment with a human review queue; no outcome figures; CTO quotedSource
SpicerrSpice-blend and substitution recommender trained on 400k–500k+ recipes (Gensim embeddings, scikit-learn), shipped as a versioned REST API powering the client's MVP; CEO quotedSource
AltermobiliParking-availability prediction and route optimization backend for a German startup's app, with a prototyped accelerometer crash-detection model; CTO quoted on user uptakeSource

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